Publications

CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Li, Shuang; Velde, K.J. Van Der; Ridder, Dick De; Dijk, Aalt D.J. Van; Soudis, Dimitrios; Zwerwer, Leslie R.; Deelen, Patrick; Hendriksen, Dennis; Charbon, Bart; Gijn, Marielle E. Van; Abbott, Kristin; Sikkema-Raddatz, Birgit; Diemen, Cleo C. Van; Kerstjens-Frederikse, Wilhelmina S.; Sinke, Richard J.; Swertz, Morris A.

Summary

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.